本文整理汇总了Python中model.common.MeanShift方法的典型用法代码示例。如果您正苦于以下问题：Python common.MeanShift方法的具体用法？Python common.MeanShift怎么用？Python common.MeanShift使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类model.common的用法示例。
在下文中一共展示了common.MeanShift方法的9个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: __init__
# 需要导入模块: from model import common [as 别名]
# 或者: from model.common import MeanShift [as 别名]
def __init__(self, conv_index, rgb_range=1):
super(VGG, self).__init__()
vgg_features = models.vgg19(pretrained=True).features
modules = [m for m in vgg_features]
if conv_index == '22':
self.vgg = nn.Sequential(*modules[:8])
elif conv_index == '54':
self.vgg = nn.Sequential(*modules[:35])
vgg_mean = (0.485, 0.456, 0.406)
vgg_std = (0.229 * rgb_range, 0.224 * rgb_range, 0.225 * rgb_range)
self.sub_mean = common.MeanShift(rgb_range, vgg_mean, vgg_std)
self.vgg.requires_grad = False 示例2: __init__
# 需要导入模块: from model import common [as 别名]
# 或者: from model.common import MeanShift [as 别名]
def __init__(self, args, conv=common.default_conv):
super(MDSR, self).__init__()
n_resblocks = args.n_resblocks
n_feats = args.n_feats
kernel_size = 3
act = nn.ReLU(True)
self.scale_idx = 0
self.url = url['r{}f{}'.format(n_resblocks, n_feats)]
self.sub_mean = common.MeanShift(args.rgb_range)
self.add_mean = common.MeanShift(args.rgb_range, sign=1)
m_head = [conv(args.n_colors, n_feats, kernel_size)]
self.pre_process = nn.ModuleList([
nn.Sequential(
common.ResBlock(conv, n_feats, 5, act=act),
common.ResBlock(conv, n_feats, 5, act=act)
) for _ in args.scale
])
m_body = [
common.ResBlock(
conv, n_feats, kernel_size, act=act
) for _ in range(n_resblocks)
]
m_body.append(conv(n_feats, n_feats, kernel_size))
self.upsample = nn.ModuleList([
common.Upsampler(conv, s, n_feats, act=False) for s in args.scale
])
m_tail = [conv(n_feats, args.n_colors, kernel_size)]
self.head = nn.Sequential(*m_head)
self.body = nn.Sequential(*m_body)
self.tail = nn.Sequential(*m_tail) 示例3: __init__
# 需要导入模块: from model import common [as 别名]
# 或者: from model.common import MeanShift [as 别名]
def __init__(self, args, conv=common.default_conv):
super(EDSR, self).__init__()
n_resblocks = args.n_resblocks
n_feats = args.n_feats
kernel_size = 3
scale = args.scale[0]
act = nn.ReLU(True)
self.sub_mean = common.MeanShift(args.rgb_range)
self.add_mean = common.MeanShift(args.rgb_range, sign=1)
# define head module
m_head = [conv(args.n_colors, n_feats, kernel_size)]
# define body module
m_body = [
common.ResBlock(
conv, n_feats, kernel_size, act=act, res_scale=args.res_scale
) for _ in range(n_resblocks)
]
m_body.append(conv(n_feats, n_feats, kernel_size))
# define tail module
m_tail = [
common.Upsampler(conv, scale, n_feats, act=False),
conv(n_feats, args.n_colors, kernel_size)
]
self.head = nn.Sequential(*m_head)
self.body = nn.Sequential(*m_body)
self.tail = nn.Sequential(*m_tail) 示例4: __init__
# 需要导入模块: from model import common [as 别名]
# 或者: from model.common import MeanShift [as 别名]
def __init__(self, args, conv=common.default_conv):
super(RCAN, self).__init__()
n_resgroups = args.n_resgroups
n_resblocks = args.n_resblocks
n_feats = args.n_feats
kernel_size = 3
reduction = args.reduction
scale = args.scale[0]
act = nn.ReLU(True)
# RGB mean for DIV2K
self.sub_mean = common.MeanShift(args.rgb_range)
# define head module
modules_head = [conv(args.n_colors, n_feats, kernel_size)]
# define body module
modules_body = [
ResidualGroup(
conv, n_feats, kernel_size, reduction, act=act, res_scale=args.res_scale, n_resblocks=n_resblocks) \
for _ in range(n_resgroups)]
modules_body.append(conv(n_feats, n_feats, kernel_size))
# define tail module
modules_tail = [
common.Upsampler(conv, scale, n_feats, act=False),
conv(n_feats, args.n_colors, kernel_size)]
self.add_mean = common.MeanShift(args.rgb_range, sign=1)
self.head = nn.Sequential(*modules_head)
self.body = nn.Sequential(*modules_body)
self.tail = nn.Sequential(*modules_tail) 示例5: __init__
# 需要导入模块: from model import common [as 别名]
# 或者: from model.common import MeanShift [as 别名]
def __init__(self, args, conv=common.default_conv):
super(EDSR, self).__init__()
n_resblocks = args.n_resblocks
n_feats = args.n_feats
kernel_size = 3
scale = args.scale[0]
act = nn.ReLU(True)
# self.url = url['r{}f{}x{}'.format(n_resblocks, n_feats, scale)]
self.sub_mean = common.MeanShift(args.rgb_range)
self.add_mean = common.MeanShift(args.rgb_range, sign=1)
# define head module
m_head = [conv(args.n_colors, n_feats, kernel_size)]
# define body module
m_body = [
common.ResBlock(
conv, n_feats, kernel_size, act=act, res_scale=args.res_scale
) for _ in range(n_resblocks)
]
m_body.append(conv(n_feats, n_feats, kernel_size))
# define tail module
m_tail = [
common.Upsampler(conv, scale, n_feats, act=False),
conv(n_feats, args.n_colors, kernel_size)
]
self.head = nn.Sequential(*m_head)
self.body = nn.Sequential(*m_body)
self.tail = nn.Sequential(*m_tail) 示例6: __init__
# 需要导入模块: from model import common [as 别名]
# 或者: from model.common import MeanShift [as 别名]
def __init__(self, conv_index, rgb_range=1):
super(VGG, self).__init__()
vgg_features = models.vgg19(pretrained=True).features
modules = [m for m in vgg_features]
if conv_index.find('22') >= 0:
self.vgg = nn.Sequential(*modules[:8])
elif conv_index.find('54') >= 0:
self.vgg = nn.Sequential(*modules[:35])
vgg_mean = (0.485, 0.456, 0.406)
vgg_std = (0.229 * rgb_range, 0.224 * rgb_range, 0.225 * rgb_range)
self.sub_mean = common.MeanShift(rgb_range, vgg_mean, vgg_std)
for p in self.parameters():
p.requires_grad = False 示例7: __init__
# 需要导入模块: from model import common [as 别名]
# 或者: from model.common import MeanShift [as 别名]
def __init__(self, args, conv=common.default_conv):
super(EDSR, self).__init__()
n_resblocks = args.n_resblocks
n_feats = args.n_feats
kernel_size = 3
scale = args.scale[0]
act = nn.ReLU(True)
url_name = 'r{}f{}x{}'.format(n_resblocks, n_feats, scale)
if url_name in url:
self.url = url[url_name]
else:
self.url = None
self.sub_mean = common.MeanShift(args.rgb_range)
self.add_mean = common.MeanShift(args.rgb_range, sign=1)
# define head module
m_head = [conv(args.n_colors, n_feats, kernel_size)]
# define body module
m_body = [
common.ResBlock(
conv, n_feats, kernel_size, act=act, res_scale=args.res_scale
) for _ in range(n_resblocks)
]
m_body.append(conv(n_feats, n_feats, kernel_size))
# define tail module
m_tail = [
common.Upsampler(conv, scale, n_feats, act=False),
conv(n_feats, args.n_colors, kernel_size)
]
self.head = nn.Sequential(*m_head)
self.body = nn.Sequential(*m_body)
self.tail = nn.Sequential(*m_tail) 示例8: __init__
# 需要导入模块: from model import common [as 别名]
# 或者: from model.common import MeanShift [as 别名]
def __init__(self, args, conv=common.default_conv):
super(EDSR, self).__init__()
n_resblock = args.n_resblocks
n_feats = args.n_feats
kernel_size = 3
scale = args.scale[0]
act = nn.ReLU(True)
rgb_mean = (0.4488, 0.4371, 0.4040)
rgb_std = (1.0, 1.0, 1.0)
self.sub_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std)
# define head module
m_head = [conv(args.n_colors, n_feats, kernel_size)]
# define body module
m_body = [
common.ResBlock(
conv, n_feats, kernel_size, act=act, res_scale=args.res_scale
) for _ in range(n_resblock)
]
m_body.append(conv(n_feats, n_feats, kernel_size))
# define tail module
m_tail = [
common.Upsampler(conv, scale, n_feats, act=False),
conv(n_feats, args.n_colors, kernel_size)
]
self.add_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std, 1)
self.head = nn.Sequential(*m_head)
self.body = nn.Sequential(*m_body)
self.tail = nn.Sequential(*m_tail)
# from IPython import embed; embed(); exit() 示例9: __init__
# 需要导入模块: from model import common [as 别名]
# 或者: from model.common import MeanShift [as 别名]
def __init__(self, args):
super(CARN, self).__init__()
#scale = kwargs.get("scale")
#multi_scale = kwargs.get("multi_scale")
#group = kwargs.get("group", 1)
multi_scale = len(args.scale) > 1
self.scale_idx = 0
scale = args.scale[self.scale_idx]
group = 1
self.scale = args.scale
rgb_mean = (0.4488, 0.4371, 0.4040)
rgb_std = (1.0, 1.0, 1.0)
self.sub_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std)
self.add_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std, 1)
#self.sub_mean = ops.MeanShift((0.4488, 0.4371, 0.4040), sub=True)
#self.add_mean = ops.MeanShift((0.4488, 0.4371, 0.4040), sub=False)
self.entry = nn.Conv2d(3, 64, 3, 1, 1)
self.b1 = Block(64, 64)
self.b2 = Block(64, 64)
self.b3 = Block(64, 64)
self.c1 = ops.BasicBlock(64*2, 64, 1, 1, 0)
self.c2 = ops.BasicBlock(64*3, 64, 1, 1, 0)
self.c3 = ops.BasicBlock(64*4, 64, 1, 1, 0)
self.upsample = ops.UpsampleBlock(64, scale=scale,
multi_scale=multi_scale,
group=group)
self.exit = nn.Conv2d(64, 3, 3, 1, 1)